fMRI task analyses
2024-02-02
This code reproduces the fMRI analyses reported in the following manuscript:
Mindful attention to alcohol can reduce cravings in the moment and consumption in daily life
load packages
library(pacman)
pacman::p_load(tidyverse, brms, ggeffects, tidybayes, ROCR, caret, interactions, modelr, broom.mixed, performance, install = TRUE)
devtools::install_github("hadley/emo")define aesthetics
palette = c("#e64626", "#1985a1", "#4c5c68", "#FAC748")
palette_group = c(palette[2], palette[4])
plot_aes = theme_minimal() +
theme(legend.position = "top",
legend.text = element_text(size = 16),
plot.title = element_text(hjust = 0.5),
text = element_text(size = 18, family = "Futura Medium"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text = element_text(color = "black"),
axis.line = element_line(colour = "black"),
axis.ticks.y = element_blank())define functions
make_table = function(data) {
data %>%
broom.mixed::tidy(conf.int = TRUE, conf.level = 0.9) %>%
filter(effect == "fixed") %>%
mutate(term = gsub("\\(Intercept\\)", "intercept", term),
term = gsub("trial_condregulation", "task condition (mindful attention)", term),
term = gsub("trial_cond_recode", "task condition (mindful attention)", term),
term = gsub("trial_condmindfulattention", "task condition (mindful attention)", term),
term = gsub("dot_between_std_noc", "signature expression (between)", term),
term = gsub("dot_within_std", "signature expression (within)", term),
term = gsub("dot_sd", "signature expression variability", term),
term = gsub("regulation_expression", "signature expression", term),
term = gsub("confidence_rating", "confidence rating", term),
term = gsub("MAAS_mean", "MAAS score", term),
term = gsub(":", " x ", term),
`b [90% CI]` = sprintf("%.2f [%.2f, %.2f]", estimate, conf.low, conf.high),) %>%
select(term, `b [90% CI]`) %>%
knitr::kable(digits = 2)
}load data
classifier_data = read.csv("../data/classifier_data.csv", stringsAsFactors = FALSE) %>%
mutate(predicted_factor = as.factor(predicted_factor),
actual_factor = as.factor(actual_factor))
ratings = read.csv("../data/ratings.csv", stringsAsFactors = FALSE)
maas = read.csv("../data/MAAS.csv", stringsAsFactors = FALSE)
merged = read.csv("../data/task_neuro_data.csv", stringsAsFactors = FALSE)
disaggregated = read.csv("../data/disaggregated_data.csv", stringsAsFactors = FALSE)
disaggregated_mindful = disaggregated %>%
filter(condition == "mindful attention")mindful attention signature development analyses
Here is the weight map from the MVPA analyses:
cross-validation accuracy
✅ We expect that we will be able to train a classifier at the run level to distinguish mindful attention from uninstructed reactivity to alcohol cues with greater than chance accuracy decoding.
caret::confusionMatrix(classifier_data$predicted_factor, classifier_data$actual_factor)## Confusion Matrix and Statistics
##
## Reference
## Prediction react regulate
## react 59 45
## regulate 52 66
##
## Accuracy : 0.5631
## 95% CI : (0.4951, 0.6293)
## No Information Rate : 0.5
## P-Value [Acc > NIR] : 0.03486
##
## Kappa : 0.1261
##
## Mcnemar's Test P-Value : 0.54239
##
## Sensitivity : 0.5315
## Specificity : 0.5946
## Pos Pred Value : 0.5673
## Neg Pred Value : 0.5593
## Prevalence : 0.5000
## Detection Rate : 0.2658
## Detection Prevalence : 0.4685
## Balanced Accuracy : 0.5631
##
## 'Positive' Class : react
## trial-level accuracy
✅ Given that the classifier is developed at the run level, we will also confirm that the expression of the mindful attention signature is evident at the trial-level. That is, we expect the signature expression to be higher during mindful attention trials compared to reactivity trials.
means
plot
data_means = merged %>%
filter(condition == "mindful attention") %>%
mutate(trial_cond = gsub("regulation", "mindful attention", trial_cond),
trial_cond = factor(trial_cond, levels = c("reactivity", "mindful attention")),
dot_std = scale(dot, center = TRUE, scale = TRUE))
data_means %>%
ggplot(aes(trial_cond, dot, color = trial_cond, fill = trial_cond)) +
stat_summary(fun.data = "mean_cl_boot", geom = "bar") +
stat_summary(fun.data = "mean_cl_boot", geom = "errorbar", width = 0, color = "black") +
scale_color_manual(name = "", values = palette) +
scale_fill_manual(name = "", values = palette) +
labs(x = "\ntrial type", y = "signature expression\n") +
plot_aes +
theme(legend.position = "none")
run model
prior = c(prior(normal(0, 1), class=b))
mod_means = brms::brm(dot_std ~ trial_cond + (1 + trial_cond | pID),
data = data_means,
prior = prior,
cores = 8, iter = 1000, silent = TRUE, seed = 6523)table
make_table(mod_means)| term | b [90% CI] |
|---|---|
| intercept | -0.41 [-0.45, -0.36] |
| task condition (mindful attention) | 0.85 [0.79, 0.91] |
summary
summary(mod_means, prob = .9)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: dot_std ~ trial_cond + (1 + trial_cond | pID)
## Data: data_means (Number of observations: 2245)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Group-Level Effects:
## ~pID (Number of levels: 37)
## Estimate Est.Error l-90% CI u-90% CI
## sd(Intercept) 0.03 0.02 0.00 0.07
## sd(trial_condmindfulattention) 0.04 0.03 0.00 0.09
## cor(Intercept,trial_condmindfulattention) -0.17 0.58 -0.95 0.87
## Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.00 1047 874
## sd(trial_condmindfulattention) 1.00 1067 1057
## cor(Intercept,trial_condmindfulattention) 1.00 1873 1446
##
## Population-Level Effects:
## Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS
## Intercept -0.41 0.03 -0.45 -0.36 1.01 3630
## trial_condmindfulattention 0.85 0.04 0.79 0.91 1.01 3509
## Tail_ESS
## Intercept 1413
## trial_condmindfulattention 1288
##
## Family Specific Parameters:
## Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.91 0.01 0.88 0.93 1.00 3844 993
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).ROC plot
acc_line1 = data.frame(x = c(0, 1), y = c(1, 1))
acc_line2 = data.frame(y = c(0, 1), x = c(0, 0))
merged %>%
filter(condition %in% c("mindful attention")) %>%
filter(!is.na(dot)) %>%
mutate(actual = ifelse(trial_cond == "regulation", 1, 0)) %>%
group_by(condition) %>%
do({
condition = .$condition
pred = prediction(.$dot, .$actual)
perf = ROCR::performance(pred, measure = "tpr", x.measure = "fpr")
data.frame(cut = perf@alpha.values[[1]],fpr = perf@x.values[[1]],tpr = perf@y.values[[1]])
}) %>%
ggplot(aes(fpr, tpr)) +
geom_line(aes(color = condition), size = 1) +
geom_abline(intercept = 0, slope = 1) +
geom_line(data = acc_line1, aes(x, y)) +
geom_line(data = acc_line2, aes(x, y)) +
scale_color_manual(values = palette_group) +
scale_x_continuous(breaks = seq(0, 1, .2)) +
scale_y_continuous(breaks = seq(0, 1, .2)) +
labs(x = "\nfalse positive rate (1 - specificity)", y = "true positive rate (sensitivity)\n") +
plot_aes +
theme(legend.position = "none",
legend.spacing.y = unit(-.1, "cm"))
confusion matrix
conf_data = merged %>%
filter(condition == "mindful attention") %>%
filter(!is.na(dot)) %>%
mutate(predicted = ifelse(dot > 0, "regulation",
ifelse(dot < 0, "reactivity", NA)),
predicted = as.factor(predicted),
trial_cond = as.factor(as.character(trial_cond)))
caret::confusionMatrix(conf_data$predicted, conf_data$trial_cond)## Confusion Matrix and Statistics
##
## Reference
## Prediction reactivity regulation
## reactivity 827 333
## regulation 346 739
##
## Accuracy : 0.6976
## 95% CI : (0.6781, 0.7165)
## No Information Rate : 0.5225
## P-Value [Acc > NIR] : <0.0000000000000002
##
## Kappa : 0.3942
##
## Mcnemar's Test P-Value : 0.6451
##
## Sensitivity : 0.7050
## Specificity : 0.6894
## Pos Pred Value : 0.7129
## Neg Pred Value : 0.6811
## Prevalence : 0.5225
## Detection Rate : 0.3684
## Detection Prevalence : 0.5167
## Balanced Accuracy : 0.6972
##
## 'Positive' Class : reactivity
## descriptive statistics
craving
disaggregated_mindful %>%
group_by(trial_cond) %>%
summarize(M = mean(resp, na.rm = TRUE),
SD = sd(resp, na.rm = TRUE)) %>%
knitr::kable(digits = 1, format = "pandoc")| trial_cond | M | SD |
|---|---|---|
| reactivity | 2.0 | 1.1 |
| regulation | 1.9 | 1.0 |
efficacy: trial-level craving analyses
H1
✅ Compared to reactivity trials, mindful attention trials will be associated with decreased craving
run model
mod_behavior = brms::brm(resp ~ 1 + trial_cond +
(1 + trial_cond | pID) + (1 | stimulus),
disaggregated_mindful,
prior = prior,
cores = 8, iter = 1000, silent = TRUE, seed = 6523)plot
mod_behavior %>%
spread_draws(b_Intercept, b_trial_condregulation) %>%
mutate(reactivity = b_Intercept,
mindfulness = b_Intercept + b_trial_condregulation) %>%
gather(`trial type`, value, reactivity, mindfulness) %>%
ggplot(aes(y = "", x = value, fill = `trial type`)) +
stat_halfeye(alpha = .5) +
scale_fill_manual(values = c(palette[2], palette[1])) +
scale_y_discrete(expand = c(.1, .1)) +
coord_cartesian(xlim = c(1.5, 2.5)) +
labs(x = "\npredicted craving rating\n", y = "") +
plot_aes
table
make_table(mod_behavior)| term | b [90% CI] |
|---|---|
| intercept | 2.01 [1.82, 2.20] |
| task condition (mindful attention) | -0.12 [-0.21, -0.03] |
summary
summary(mod_behavior, prob = .9)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: resp ~ 1 + trial_cond + (1 + trial_cond | pID) + (1 | stimulus)
## Data: disaggregated_mindful (Number of observations: 2214)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Group-Level Effects:
## ~pID (Number of levels: 37)
## Estimate Est.Error l-90% CI u-90% CI Rhat
## sd(Intercept) 0.60 0.08 0.49 0.74 1.01
## sd(trial_condregulation) 0.21 0.07 0.09 0.32 1.02
## cor(Intercept,trial_condregulation) -0.10 0.26 -0.49 0.32 1.01
## Bulk_ESS Tail_ESS
## sd(Intercept) 489 728
## sd(trial_condregulation) 328 191
## cor(Intercept,trial_condregulation) 1356 867
##
## ~stimulus (Number of levels: 72)
## Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.39 0.04 0.33 0.46 1.01 422 705
##
## Population-Level Effects:
## Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS
## Intercept 2.01 0.12 1.82 2.20 1.02 279
## trial_condregulation -0.12 0.05 -0.21 -0.03 1.00 1391
## Tail_ESS
## Intercept 670
## trial_condregulation 1452
##
## Family Specific Parameters:
## Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.85 0.01 0.83 0.87 1.00 2916 1343
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).ICC
participant
performance::variance_decomposition(mod_behavior, re_formula = ~ (1 + trial_cond | pID))## # Random Effect Variances and ICC
##
## Conditioned on: ~(1 + trial_cond | pID)
##
## ## Variance Ratio (comparable to ICC)
## Ratio: 0.32 CI 95%: [0.25 0.37]
##
## ## Variances of Posterior Predicted Distribution
## Conditioned on fixed effects: 0.72 CI 95%: [0.67 0.79]
## Conditioned on rand. effects: 1.06 CI 95%: [0.98 1.14]
##
## ## Difference in Variances
## Difference: 0.33 CI 95%: [0.25 0.42]stimulus
performance::variance_decomposition(mod_behavior, re_formula = ~ (1 | stimulus))## # Random Effect Variances and ICC
##
## Conditioned on: ~(1 | stimulus)
##
## ## Variance Ratio (comparable to ICC)
## Ratio: 0.17 CI 95%: [0.09 0.24]
##
## ## Variances of Posterior Predicted Distribution
## Conditioned on fixed effects: 0.72 CI 95%: [0.67 0.79]
## Conditioned on rand. effects: 0.87 CI 95%: [0.81 0.95]
##
## ## Difference in Variances
## Difference: 0.15 CI 95%: [0.08 0.22]H2
✅ A: We expect that people who have greater expression of the mindful attention signature on average (i.e., L2, between-person expression) will also have lower craving ratings on a trial-by-trial basis.
❌ B: We expect that trials with greater expression of the mindful attention signature compared to one’s average (i.e., L1, within-person expression) will be associated with lower craving ratings on a trial-by-trial basis.
run model
mod_h2_int = brms::brm(resp ~ 1 + trial_cond * dot_between_std_noc +
trial_cond * dot_within_std +
(1 + trial_cond * dot_within_std | pID) + (1 | stimulus),
disaggregated_mindful,
prior = prior,
cores = 8, iter = 1000, silent = TRUE, seed = 6523)plots
between-person
points_between = disaggregated_mindful %>%
select(pID, resp, trial_cond, dot_between_std_noc) %>%
group_by(pID, trial_cond, dot_between_std_noc) %>%
summarize(resp = mean(resp, na.rm = TRUE)) %>%
mutate(group = ifelse(trial_cond == "regulation", "mindful attention", "reactivity"),
group = factor(group, levels = c("reactivity", "mindful attention")),
type = "between-person") %>%
rename("x" = dot_between_std_noc,
"predicted" = resp)
vals = modelr::seq_range(points_between$x, n = 25)
predicted = ggeffects::ggpredict(mod_h2_int, c("dot_between_std_noc [vals]", "trial_cond"),
ci.lvl = 0.9) %>%
data.frame() %>%
mutate(type = "between-person") %>%
mutate(group = ifelse(group == "regulation", "mindful attention", "reactivity"),
group = factor(group, levels = c("reactivity", "mindful attention")))
(between = predicted %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
geom_point(data = points_between, alpha = .4, size = 2) +
geom_line(data = points_between, aes(group = pID), alpha = .4, size = 1.25, color = "grey") +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(size = 2) +
scale_fill_manual(name = "trial type", values = palette) +
scale_color_manual(name = "trial type", values = palette) +
scale_y_continuous(breaks = c(1,2,3)) +
labs(x = expression("\nreactivity " * symbol('\254') * " signature expression " * symbol('\256') * " mindful attention"),
y = "craving rating\n",
title = "between-person") +
plot_aes +
theme(legend.position = "top"))
within-person
points_within = disaggregated_mindful %>%
select(pID, resp, trial_cond, dot_within_std) %>%
mutate(group = ifelse(trial_cond == "regulation", "mindful attention", "reactivity"),
group = factor(group, levels = c("reactivity", "mindful attention")),
type = "within-person") %>%
rename("x" = dot_within_std,
"predicted" = resp)
vals = modelr::seq_range(points_within$x, n = 25)
predicted = ggeffects::ggpredict(mod_h2_int, c("dot_within_std [vals]", "trial_cond"),
ci.lvl = 0.9) %>%
data.frame() %>%
mutate(group = ifelse(group == "regulation", "mindful attention", "reactivity"),
group = factor(group, levels = c("reactivity", "mindful attention")))
(within = predicted %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
stat_smooth(data = points_within, aes(group = interaction(pID, group)), geom = "line", method = "lm", alpha = 0.15, se = FALSE, size = 1.25, fullrange = TRUE) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .3, color = NA) +
geom_line(size = 2) +
scale_fill_manual(name = "trial type", values = palette) +
scale_color_manual(name = "trial type", values = palette) +
labs(x = "within-person signature expression (SD)",
y = "craving rating\n",
title = "within-person") +
plot_aes +
theme(legend.position = "top"))
combined
ggpubr::ggarrange(between, within, nrow = 1, common.legend = TRUE)
table
make_table(mod_h2_int)| term | b [90% CI] |
|---|---|
| intercept | 2.27 [1.97, 2.57] |
| task condition (mindful attention) | -0.20 [-0.57, 0.17] |
| signature expression (between) | 0.25 [0.03, 0.48] |
| signature expression (within) | -0.01 [-0.06, 0.03] |
| task condition (mindful attention) x signature expression (between) | -0.45 [-0.84, -0.06] |
| task condition (mindful attention) x signature expression (within) | -0.01 [-0.08, 0.06] |
simple slopes
emmeans::emtrends(mod_h2_int, ~ trial_cond, var="dot_between_std_noc", level = 0.9) %>%
data.frame() %>%
mutate(`b [90% CI]` = sprintf("%.2f [%.2f, %.2f]", dot_between_std_noc.trend, lower.HPD, upper.HPD),
trial_cond = recode(trial_cond, "regulation" = "mindful attention")) %>%
select(trial_cond, `b [90% CI]`) %>%
knitr::kable()| trial_cond | b [90% CI] |
|---|---|
| reactivity | 0.25 [0.05, 0.49] |
| mindful attention | -0.20 [-0.51, 0.12] |
emmeans::emtrends(mod_h2_int, ~ trial_cond, var="dot_within_std", level = 0.9) %>%
data.frame() %>%
mutate(`b [90% CI]` = sprintf("%.2f [%.2f, %.2f]", dot_within_std.trend, lower.HPD, upper.HPD),
trial_cond = recode(trial_cond, "regulation" = "mindful attention")) %>%
select(trial_cond, `b [90% CI]`) %>%
knitr::kable()| trial_cond | b [90% CI] |
|---|---|
| reactivity | -0.01 [-0.06, 0.03] |
| mindful attention | -0.03 [-0.08, 0.03] |
summary
summary(mod_h2_int, prob = .9)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: resp ~ 1 + trial_cond * dot_between_std_noc + trial_cond * dot_within_std + (1 + trial_cond * dot_within_std | pID) + (1 | stimulus)
## Data: disaggregated_mindful (Number of observations: 2193)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Group-Level Effects:
## ~pID (Number of levels: 37)
## Estimate
## sd(Intercept) 0.59
## sd(trial_condregulation) 0.17
## sd(dot_within_std) 0.04
## sd(trial_condregulation:dot_within_std) 0.10
## cor(Intercept,trial_condregulation) 0.01
## cor(Intercept,dot_within_std) 0.22
## cor(trial_condregulation,dot_within_std) 0.06
## cor(Intercept,trial_condregulation:dot_within_std) -0.37
## cor(trial_condregulation,trial_condregulation:dot_within_std) 0.19
## cor(dot_within_std,trial_condregulation:dot_within_std) -0.17
## Est.Error
## sd(Intercept) 0.08
## sd(trial_condregulation) 0.07
## sd(dot_within_std) 0.03
## sd(trial_condregulation:dot_within_std) 0.05
## cor(Intercept,trial_condregulation) 0.27
## cor(Intercept,dot_within_std) 0.41
## cor(trial_condregulation,dot_within_std) 0.43
## cor(Intercept,trial_condregulation:dot_within_std) 0.31
## cor(trial_condregulation,trial_condregulation:dot_within_std) 0.39
## cor(dot_within_std,trial_condregulation:dot_within_std) 0.44
## l-90% CI u-90% CI
## sd(Intercept) 0.48 0.73
## sd(trial_condregulation) 0.03 0.29
## sd(dot_within_std) 0.00 0.10
## sd(trial_condregulation:dot_within_std) 0.02 0.18
## cor(Intercept,trial_condregulation) -0.41 0.47
## cor(Intercept,dot_within_std) -0.53 0.80
## cor(trial_condregulation,dot_within_std) -0.67 0.76
## cor(Intercept,trial_condregulation:dot_within_std) -0.82 0.18
## cor(trial_condregulation,trial_condregulation:dot_within_std) -0.49 0.77
## cor(dot_within_std,trial_condregulation:dot_within_std) -0.82 0.62
## Rhat Bulk_ESS
## sd(Intercept) 1.01 571
## sd(trial_condregulation) 1.03 183
## sd(dot_within_std) 1.00 783
## sd(trial_condregulation:dot_within_std) 1.01 611
## cor(Intercept,trial_condregulation) 1.00 1620
## cor(Intercept,dot_within_std) 1.00 2041
## cor(trial_condregulation,dot_within_std) 1.01 1891
## cor(Intercept,trial_condregulation:dot_within_std) 1.00 1839
## cor(trial_condregulation,trial_condregulation:dot_within_std) 1.01 1077
## cor(dot_within_std,trial_condregulation:dot_within_std) 1.00 1257
## Tail_ESS
## sd(Intercept) 717
## sd(trial_condregulation) 69
## sd(dot_within_std) 750
## sd(trial_condregulation:dot_within_std) 585
## cor(Intercept,trial_condregulation) 902
## cor(Intercept,dot_within_std) 1341
## cor(trial_condregulation,dot_within_std) 1454
## cor(Intercept,trial_condregulation:dot_within_std) 1165
## cor(trial_condregulation,trial_condregulation:dot_within_std) 1298
## cor(dot_within_std,trial_condregulation:dot_within_std) 1463
##
## ~stimulus (Number of levels: 72)
## Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.39 0.04 0.33 0.45 1.00 716 1109
##
## Population-Level Effects:
## Estimate Est.Error l-90% CI u-90% CI
## Intercept 2.27 0.18 1.97 2.57
## trial_condregulation -0.20 0.23 -0.57 0.17
## dot_between_std_noc 0.25 0.14 0.03 0.48
## dot_within_std -0.01 0.03 -0.06 0.03
## trial_condregulation:dot_between_std_noc -0.45 0.24 -0.84 -0.06
## trial_condregulation:dot_within_std -0.01 0.04 -0.08 0.06
## Rhat Bulk_ESS Tail_ESS
## Intercept 1.01 658 848
## trial_condregulation 1.00 1522 1528
## dot_between_std_noc 1.00 1080 1289
## dot_within_std 1.00 2387 1594
## trial_condregulation:dot_between_std_noc 1.00 1373 1268
## trial_condregulation:dot_within_std 1.00 2117 1672
##
## Family Specific Parameters:
## Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.85 0.01 0.83 0.87 1.00 2904 1425
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).ICC
participant
performance::variance_decomposition(mod_h2_int, re_formula = ~ (1 + trial_cond * dot_within_std | pID))## # Random Effect Variances and ICC
##
## Conditioned on: ~(1 + trial_cond * dot_within_std | pID)
##
## ## Variance Ratio (comparable to ICC)
## Ratio: 0.31 CI 95%: [0.25 0.37]
##
## ## Variances of Posterior Predicted Distribution
## Conditioned on fixed effects: 0.73 CI 95%: [0.67 0.80]
## Conditioned on rand. effects: 1.06 CI 95%: [0.98 1.14]
##
## ## Difference in Variances
## Difference: 0.33 CI 95%: [0.25 0.41]stimulus
performance::variance_decomposition(mod_h2_int, re_formula = ~ (1 | stimulus))## # Random Effect Variances and ICC
##
## Conditioned on: ~(1 | stimulus)
##
## ## Variance Ratio (comparable to ICC)
## Ratio: 0.17 CI 95%: [0.09 0.24]
##
## ## Variances of Posterior Predicted Distribution
## Conditioned on fixed effects: 0.73 CI 95%: [0.67 0.79]
## Conditioned on rand. effects: 0.88 CI 95%: [0.81 0.95]
##
## ## Difference in Variances
## Difference: 0.15 CI 95%: [0.08 0.22]sensitivity analyses
mindful signature attention: discriminant validity
To examine discriminant validity, we will apply the mindful attention signature to data from a separate group of participants who were instructed to use a different form of cognitive regulation (i.e., not mindful attention) that is not expected to rely on the same brain regions. We expect lower than chance accuracy decoding alcohol regulation versus reactivity trials.
ROC plot
acc_line1 = data.frame(x = c(0, 1), y = c(1, 1))
acc_line2 = data.frame(y = c(0, 1), x = c(0, 0))
merged %>%
filter(condition %in% c("perspective-taking", "mindful attention")) %>%
filter(!is.na(dot)) %>%
mutate(actual = ifelse(trial_cond == "regulation", 1, 0)) %>%
group_by(condition) %>%
do({
condition = .$condition
pred = prediction(.$dot, .$actual)
perf = ROCR::performance(pred, measure = "tpr", x.measure = "fpr")
data.frame(cut = perf@alpha.values[[1]],fpr = perf@x.values[[1]],tpr = perf@y.values[[1]])
}) %>%
ggplot(aes(fpr, tpr)) +
geom_line(aes(color = condition), size = 1) +
geom_abline(intercept = 0, slope = 1) +
geom_line(data = acc_line1, aes(x, y)) +
geom_line(data = acc_line2, aes(x, y)) +
scale_x_continuous(breaks = seq(0, 1, .2)) +
scale_y_continuous(breaks = seq(0, 1, .2)) +
scale_color_manual(name = "", values = palette_group) +
labs(x = "\nfalse positive rate (1 - specificity)", y = "true positive rate (sensitivity)\n") +
plot_aes +
theme(legend.position = c(.75, .15),
legend.spacing.y = unit(-.1, "cm"))
confusion matrix
conf_data = merged %>%
filter(condition == "perspective-taking") %>%
filter(!is.na(dot)) %>%
mutate(predicted = ifelse(dot > 0, "regulation",
ifelse(dot < 0, "reactivity", NA)),
predicted = as.factor(predicted),
trial_cond = as.factor(as.character(trial_cond)))
caret::confusionMatrix(conf_data$predicted, conf_data$trial_cond)## Confusion Matrix and Statistics
##
## Reference
## Prediction reactivity regulation
## reactivity 469 419
## regulation 341 391
##
## Accuracy : 0.5309
## 95% CI : (0.5062, 0.5554)
## No Information Rate : 0.5
## P-Value [Acc > NIR] : 0.006941
##
## Kappa : 0.0617
##
## Mcnemar's Test P-Value : 0.005221
##
## Sensitivity : 0.5790
## Specificity : 0.4827
## Pos Pred Value : 0.5282
## Neg Pred Value : 0.5342
## Prevalence : 0.5000
## Detection Rate : 0.2895
## Detection Prevalence : 0.5481
## Balanced Accuracy : 0.5309
##
## 'Positive' Class : reactivity
## confidence rating analyses
merged_ratings = disaggregated_mindful %>%
left_join(., ratings) %>%
left_join(., maas)correlation between confidence and signature expression
corr_data = disaggregated_mindful %>%
filter(trial_cond == "regulation") %>%
select(pID, dot_between) %>%
unique() %>%
left_join(., ratings)
cor.test(corr_data$confidence_rating, corr_data$dot_between)##
## Pearson's product-moment correlation
##
## data: corr_data$confidence_rating and corr_data$dot_between
## t = 0.16437, df = 31, p-value = 0.8705
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3170124 0.3690782
## sample estimates:
## cor
## 0.02950807signature expression ~ confidence ratings
We examined the degree to which confidence ratings were associated with neural signature expression
run model
mod_ph_expression = brms::brm(dot ~ confidence_rating * trial_cond + (1 + trial_cond | pID),
data = merged_ratings,
prior = prior,
cores = 8, iter = 1000, silent = TRUE, seed = 6523)plot
ggeffects::ggpredict(mod_ph_expression, terms = c("confidence_rating", "trial_cond"),
ci.lvl = 0.9) %>%
data.frame() %>%
mutate(group = ifelse(group == "regulation", "mindful attention", "reactivity"),
group = factor(group, levels = c("reactivity", "mindful attention"))) %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(size = 2) +
scale_fill_manual(name = "trial type", values = palette) +
scale_color_manual(name = "trial type", values = palette) +
labs(x = "instruction confidence rating (SD)",
y = "signature expression\n") +
plot_aes +
theme(legend.position = "top")
table
make_table(mod_ph_expression)| term | b [90% CI] |
|---|---|
| intercept | -1.80 [-3.95, 0.37] |
| confidence rating | 0.04 [-0.79, 0.85] |
| task condition (mindful attention) | 3.08 [1.23, 5.02] |
| confidence rating x task condition (mindful attention) | 0.01 [-1.30, 1.27] |
simple slopes
emmeans::emtrends(mod_ph_expression, ~ trial_cond, var="confidence_rating", level = .9) %>%
data.frame() %>%
mutate(`b [90% CI]` = sprintf("%.2f [%.2f, %.2f]", confidence_rating.trend, lower.HPD, upper.HPD),
trial_cond = recode(trial_cond, "regulation" = "mindful attention")) %>%
select(trial_cond, `b [90% CI]`) %>%
knitr::kable()| trial_cond | b [90% CI] |
|---|---|
| reactivity | 0.04 [-0.81, 0.81] |
| mindful attention | 0.06 [-0.86, 0.93] |
summary
summary(mod_ph_expression, prob = .9)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: dot ~ confidence_rating * trial_cond + (1 + trial_cond | pID)
## Data: merged_ratings (Number of observations: 2086)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Group-Level Effects:
## ~pID (Number of levels: 33)
## Estimate Est.Error l-90% CI u-90% CI Rhat
## sd(Intercept) 5.53 1.60 3.10 8.32 1.00
## sd(trial_condregulation) 10.19 1.82 7.51 13.31 1.00
## cor(Intercept,trial_condregulation) -0.99 0.02 -1.00 -0.95 1.01
## Bulk_ESS Tail_ESS
## sd(Intercept) 500 722
## sd(trial_condregulation) 644 1045
## cor(Intercept,trial_condregulation) 483 559
##
## Population-Level Effects:
## Estimate Est.Error l-90% CI u-90% CI
## Intercept -1.80 1.32 -3.95 0.37
## confidence_rating 0.04 0.50 -0.79 0.85
## trial_condregulation 3.08 1.14 1.23 5.02
## confidence_rating:trial_condregulation 0.01 0.79 -1.30 1.27
## Rhat Bulk_ESS Tail_ESS
## Intercept 1.01 561 884
## confidence_rating 1.00 1360 1509
## trial_condregulation 1.00 1234 1210
## confidence_rating:trial_condregulation 1.00 1270 1218
##
## Family Specific Parameters:
## Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma 14.29 0.22 13.93 14.64 1.00 5279 1564
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).H2 controlling for confidence ratings
We examined the degree to which controlling for confidence ratings affected the results for H2
run model
mod_ph_h2 = brms::brm(resp ~ 1 + trial_cond * dot_between_std_noc +
trial_cond * dot_within_std +
trial_cond * confidence_rating +
(1 + trial_cond * dot_within_std | pID) + (1 | stimulus),
data = merged_ratings,
prior = prior,
cores = 8, iter = 1000, silent = TRUE, seed = 6523)plots
between-person
points_between = disaggregated_mindful %>%
select(pID, resp, trial_cond, dot_between_std_noc) %>%
group_by(pID, trial_cond, dot_between_std_noc) %>%
summarize(resp = mean(resp, na.rm = TRUE)) %>%
mutate(group = ifelse(trial_cond == "regulation", "mindful attention", "reactivity"),
group = factor(group, levels = c("reactivity", "mindful attention")),
type = "between-person") %>%
rename("x" = dot_between_std_noc,
"predicted" = resp)
vals = modelr::seq_range(points_between$x, n = 25)
predicted = ggeffects::ggpredict(mod_ph_h2, c("dot_between_std_noc [vals]", "trial_cond"),
ci.lvl = 0.9) %>%
data.frame() %>%
mutate(type = "between-person") %>%
mutate(group = ifelse(group == "regulation", "mindful attention", "reactivity"),
group = factor(group, levels = c("reactivity", "mindful attention")))
predicted %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
geom_point(data = points_between, alpha = .4, size = 2) +
geom_line(data = points_between, aes(group = pID), alpha = .4, size = 1.5, color = "grey") +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(size = 2) +
scale_fill_manual(name = "trial type", values = palette) +
scale_color_manual(name = "trial type", values = palette) +
labs(x = expression("\nreactivity " * symbol('\254') * " signature expression " * symbol('\256') * " mindful attention"),
y = "craving rating\n") +
plot_aes +
theme(legend.position = "top")
within-person only
points_within = disaggregated_mindful %>%
select(pID, resp, trial_cond, dot_within_std) %>%
mutate(group = ifelse(trial_cond == "regulation", "mindful attention", "reactivity"),
group = factor(group, levels = c("reactivity", "mindful attention")),
type = "within-person") %>%
rename("x" = dot_within_std,
"predicted" = resp)
vals = modelr::seq_range(points_within$x, n = 25)
predicted = ggeffects::ggpredict(mod_ph_h2, c("dot_within_std [vals]", "trial_cond"),
ci.lvl = 0.9) %>%
data.frame() %>%
mutate(group = ifelse(group == "regulation", "mindful attention", "reactivity"),
group = factor(group, levels = c("reactivity", "mindful attention")))
predicted %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
stat_smooth(data = points_within, aes(group = interaction(pID, group)), geom = "line", method = "lm", alpha = 0.15, se = FALSE, size = 1.25, fullrange = TRUE) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .3, color = NA) +
geom_line(size = 2) +
scale_fill_manual(name = "trial type", values = palette) +
scale_color_manual(name = "trial type", values = palette) +
labs(x = "within-person signature expression (SD)",
y = "craving rating\n",
title = "within-person") +
plot_aes +
theme(legend.position = "top")
table
make_table(mod_ph_h2)| term | b [90% CI] |
|---|---|
| intercept | 2.30 [2.00, 2.60] |
| task condition (mindful attention) | -0.21 [-0.60, 0.18] |
| signature expression (between) | 0.25 [0.03, 0.47] |
| signature expression (within) | -0.01 [-0.06, 0.04] |
| confidence rating | -0.02 [-0.21, 0.16] |
| task condition (mindful attention) x signature expression (between) | -0.44 [-0.79, -0.07] |
| task condition (mindful attention) x signature expression (within) | -0.02 [-0.10, 0.06] |
| task condition (mindful attention) x confidence rating | -0.12 [-0.20, -0.03] |
simple slopes
signature expression
emmeans::emtrends(mod_ph_h2, ~ trial_cond, var="dot_between_std_noc", level = .9) %>%
data.frame() %>%
mutate(`b [90% CI]` = sprintf("%.2f [%.2f, %.2f]", dot_between_std_noc.trend, lower.HPD, upper.HPD),
trial_cond = recode(trial_cond, "regulation" = "mindful attention")) %>%
select(trial_cond, `b [90% CI]`) %>%
knitr::kable()| trial_cond | b [90% CI] |
|---|---|
| reactivity | 0.25 [0.02, 0.46] |
| mindful attention | -0.19 [-0.49, 0.13] |
emmeans::emtrends(mod_ph_h2, ~ trial_cond, var="dot_within_std", level = .9) %>%
data.frame() %>%
mutate(`b [90% CI]` = sprintf("%.2f [%.2f, %.2f]", dot_within_std.trend, lower.HPD, upper.HPD),
trial_cond = recode(trial_cond, "regulation" = "mindful attention")) %>%
select(trial_cond, `b [90% CI]`) %>%
knitr::kable()| trial_cond | b [90% CI] |
|---|---|
| reactivity | -0.01 [-0.06, 0.04] |
| mindful attention | -0.03 [-0.10, 0.03] |
confidence ratings
emmeans::emtrends(mod_ph_h2, ~ trial_cond, var="confidence_rating", level = .9) %>%
data.frame() %>%
mutate(`b [90% CI]` = sprintf("%.2f [%.2f, %.2f]", confidence_rating.trend, lower.HPD, upper.HPD),
trial_cond = recode(trial_cond, "regulation" = "mindful attention")) %>%
select(trial_cond, `b [90% CI]`) %>%
knitr::kable()| trial_cond | b [90% CI] |
|---|---|
| reactivity | -0.02 [-0.20, 0.16] |
| mindful attention | -0.14 [-0.32, 0.05] |
summary
summary(mod_ph_h2, prob = .9)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: resp ~ 1 + trial_cond * dot_between_std_noc + trial_cond * dot_within_std + trial_cond * confidence_rating + (1 + trial_cond * dot_within_std | pID) + (1 | stimulus)
## Data: merged_ratings (Number of observations: 2034)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Group-Level Effects:
## ~pID (Number of levels: 33)
## Estimate
## sd(Intercept) 0.63
## sd(trial_condregulation) 0.15
## sd(dot_within_std) 0.05
## sd(trial_condregulation:dot_within_std) 0.11
## cor(Intercept,trial_condregulation) -0.11
## cor(Intercept,dot_within_std) 0.18
## cor(trial_condregulation,dot_within_std) -0.00
## cor(Intercept,trial_condregulation:dot_within_std) -0.35
## cor(trial_condregulation,trial_condregulation:dot_within_std) 0.30
## cor(dot_within_std,trial_condregulation:dot_within_std) -0.15
## Est.Error
## sd(Intercept) 0.09
## sd(trial_condregulation) 0.07
## sd(dot_within_std) 0.03
## sd(trial_condregulation:dot_within_std) 0.05
## cor(Intercept,trial_condregulation) 0.30
## cor(Intercept,dot_within_std) 0.39
## cor(trial_condregulation,dot_within_std) 0.44
## cor(Intercept,trial_condregulation:dot_within_std) 0.31
## cor(trial_condregulation,trial_condregulation:dot_within_std) 0.38
## cor(dot_within_std,trial_condregulation:dot_within_std) 0.44
## l-90% CI u-90% CI
## sd(Intercept) 0.50 0.80
## sd(trial_condregulation) 0.04 0.26
## sd(dot_within_std) 0.00 0.11
## sd(trial_condregulation:dot_within_std) 0.02 0.19
## cor(Intercept,trial_condregulation) -0.56 0.41
## cor(Intercept,dot_within_std) -0.52 0.78
## cor(trial_condregulation,dot_within_std) -0.71 0.71
## cor(Intercept,trial_condregulation:dot_within_std) -0.80 0.21
## cor(trial_condregulation,trial_condregulation:dot_within_std) -0.41 0.83
## cor(dot_within_std,trial_condregulation:dot_within_std) -0.79 0.64
## Rhat Bulk_ESS
## sd(Intercept) 1.00 704
## sd(trial_condregulation) 1.00 498
## sd(dot_within_std) 1.00 853
## sd(trial_condregulation:dot_within_std) 1.00 574
## cor(Intercept,trial_condregulation) 1.00 2920
## cor(Intercept,dot_within_std) 1.00 2211
## cor(trial_condregulation,dot_within_std) 1.00 2127
## cor(Intercept,trial_condregulation:dot_within_std) 1.00 1986
## cor(trial_condregulation,trial_condregulation:dot_within_std) 1.00 1071
## cor(dot_within_std,trial_condregulation:dot_within_std) 1.00 1172
## Tail_ESS
## sd(Intercept) 1024
## sd(trial_condregulation) 851
## sd(dot_within_std) 1160
## sd(trial_condregulation:dot_within_std) 466
## cor(Intercept,trial_condregulation) 1374
## cor(Intercept,dot_within_std) 1403
## cor(trial_condregulation,dot_within_std) 1661
## cor(Intercept,trial_condregulation:dot_within_std) 1146
## cor(trial_condregulation,trial_condregulation:dot_within_std) 1446
## cor(dot_within_std,trial_condregulation:dot_within_std) 1385
##
## ~stimulus (Number of levels: 72)
## Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.40 0.04 0.34 0.47 1.00 675 1169
##
## Population-Level Effects:
## Estimate Est.Error l-90% CI u-90% CI
## Intercept 2.30 0.18 2.00 2.60
## trial_condregulation -0.21 0.24 -0.60 0.18
## dot_between_std_noc 0.25 0.14 0.03 0.47
## dot_within_std -0.01 0.03 -0.06 0.04
## confidence_rating -0.02 0.11 -0.21 0.16
## trial_condregulation:dot_between_std_noc -0.44 0.22 -0.79 -0.07
## trial_condregulation:dot_within_std -0.02 0.05 -0.10 0.06
## trial_condregulation:confidence_rating -0.12 0.05 -0.20 -0.03
## Rhat Bulk_ESS Tail_ESS
## Intercept 1.00 811 1149
## trial_condregulation 1.00 1601 953
## dot_between_std_noc 1.00 1489 1326
## dot_within_std 1.00 3573 1542
## confidence_rating 1.00 611 950
## trial_condregulation:dot_between_std_noc 1.00 1938 1713
## trial_condregulation:dot_within_std 1.00 2390 1478
## trial_condregulation:confidence_rating 1.00 2102 1572
##
## Family Specific Parameters:
## Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.85 0.01 0.82 0.87 1.00 2692 985
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).compare posterior distributions
Compare preregistered model with the modeling controlling for confidence ratings
posterior = posterior_samples(mod_h2_int) %>%
transmute(`mindful attention`= b_dot_between_std_noc + `b_trial_condregulation:dot_between_std_noc`,
reactivity = b_dot_between_std_noc) %>%
gather(`trial type`, value) %>%
mutate(model = "original model") %>%
bind_rows(posterior_samples(mod_ph_h2) %>%
transmute(`mindful attention`= b_dot_between_std_noc + `b_trial_condregulation:dot_between_std_noc`,
reactivity = b_dot_between_std_noc) %>%
gather(`trial type`, value) %>%
mutate(model = "controlling for confidence")) %>%
mutate(model = factor(model, levels = c("original model", "controlling for confidence")))
posterior %>%
ggplot(aes(y = "", x = value, fill = model)) +
stat_halfeye(alpha = .5) +
scale_fill_manual(values = palette_group) +
facet_grid(~`trial type`) +
scale_y_discrete(expand = c(.1, .1)) +
labs(x = "\nregression coefficient\n", y = "") +
plot_aes
Mindful Attention and Awareness (MAAS) analyses
merged_ratings = disaggregated_mindful %>%
left_join(., maas)correlation between MAAS and signature expression
corr_data = disaggregated_mindful %>%
filter(trial_cond == "regulation") %>%
select(pID, dot_between) %>%
unique() %>%
left_join(., maas)
cor.test(corr_data$MAAS_mean, corr_data$dot_between)##
## Pearson's product-moment correlation
##
## data: corr_data$MAAS_mean and corr_data$dot_between
## t = 0.47248, df = 32, p-value = 0.6398
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2623152 0.4098632
## sample estimates:
## cor
## 0.08323381signature expression ~ MAAS scores
We examined the degree to which MAAS scores were associated with neural signature expression
run model
mod_ph_expression = brms::brm(dot ~ MAAS_mean * trial_cond + (1 + trial_cond | pID),
data = merged_ratings,
prior = prior,
cores = 8, iter = 1000, silent = TRUE, seed = 6523)plot
ggeffects::ggpredict(mod_ph_expression, terms = c("MAAS_mean", "trial_cond"),
ci.lvl = 0.9) %>%
data.frame() %>%
mutate(group = ifelse(group == "regulation", "mindful attention", "reactivity"),
group = factor(group, levels = c("reactivity", "mindful attention"))) %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(size = 2) +
scale_fill_manual(name = "trial type", values = palette) +
scale_color_manual(name = "trial type", values = palette) +
labs(x = "MAAS score (SD)",
y = "signature expression\n") +
plot_aes +
theme(legend.position = "top")
table
make_table(mod_ph_expression)| term | b [90% CI] |
|---|---|
| intercept | -3.12 [-5.52, -0.90] |
| MAAS score | 0.44 [-0.40, 1.23] |
| task condition (mindful attention) | 3.70 [1.70, 5.68] |
| MAAS score x task condition (mindful attention) | -0.34 [-1.77, 1.13] |
simple slopes
emmeans::emtrends(mod_ph_expression, ~ trial_cond, var="MAAS_mean", level = .9) %>%
data.frame() %>%
mutate(`b [90% CI]` = sprintf("%.2f [%.2f, %.2f]", MAAS_mean.trend, lower.HPD, upper.HPD),
trial_cond = recode(trial_cond, "regulation" = "mindful attention")) %>%
select(trial_cond, `b [90% CI]`) %>%
knitr::kable()| trial_cond | b [90% CI] |
|---|---|
| reactivity | 0.46 [-0.40, 1.22] |
| mindful attention | 0.13 [-0.96, 1.24] |
summary
summary(mod_ph_expression, prob = .9)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: dot ~ MAAS_mean * trial_cond + (1 + trial_cond | pID)
## Data: merged_ratings (Number of observations: 2245)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Group-Level Effects:
## ~pID (Number of levels: 37)
## Estimate Est.Error l-90% CI u-90% CI Rhat
## sd(Intercept) 3.92 1.51 1.48 6.43 1.05
## sd(trial_condregulation) 9.21 1.83 6.40 12.25 1.01
## cor(Intercept,trial_condregulation) -0.95 0.13 -1.00 -0.83 1.05
## Bulk_ESS Tail_ESS
## sd(Intercept) 65 207
## sd(trial_condregulation) 408 854
## cor(Intercept,trial_condregulation) 100 57
##
## Population-Level Effects:
## Estimate Est.Error l-90% CI u-90% CI Rhat
## Intercept -3.12 1.38 -5.52 -0.90 1.05
## MAAS_mean 0.44 0.49 -0.40 1.23 1.01
## trial_condregulation 3.70 1.19 1.70 5.68 1.01
## MAAS_mean:trial_condregulation -0.34 0.88 -1.77 1.13 1.00
## Bulk_ESS Tail_ESS
## Intercept 100 76
## MAAS_mean 1003 875
## trial_condregulation 678 1080
## MAAS_mean:trial_condregulation 1020 1076
##
## Family Specific Parameters:
## Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma 14.18 0.21 13.84 14.52 1.01 3240 1559
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).H2 controlling for MAAS scores
We examined the degree to which controlling for MAAS scores affected the results for H2
run model
mod_ph_h2 = brms::brm(resp ~ 1 + trial_cond * dot_between_std_noc +
trial_cond * dot_within_std +
trial_cond * MAAS_mean +
(1 + trial_cond * dot_within_std | pID) + (1 | stimulus),
data = merged_ratings,
prior = prior,
cores = 8, iter = 1000, silent = TRUE, seed = 6523)plots
between-person
points_between = disaggregated_mindful %>%
select(pID, resp, trial_cond, dot_between_std_noc) %>%
group_by(pID, trial_cond, dot_between_std_noc) %>%
summarize(resp = mean(resp, na.rm = TRUE)) %>%
mutate(group = ifelse(trial_cond == "regulation", "mindful attention", "reactivity"),
group = factor(group, levels = c("reactivity", "mindful attention")),
type = "between-person") %>%
rename("x" = dot_between_std_noc,
"predicted" = resp)
vals = modelr::seq_range(points_between$x, n = 25)
predicted = ggeffects::ggpredict(mod_ph_h2, c("dot_between_std_noc [vals]", "trial_cond"),
ci.lvl = 0.9) %>%
data.frame() %>%
mutate(type = "between-person") %>%
mutate(group = ifelse(group == "regulation", "mindful attention", "reactivity"),
group = factor(group, levels = c("reactivity", "mindful attention")))
predicted %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
geom_point(data = points_between, alpha = .4, size = 2) +
geom_line(data = points_between, aes(group = pID), alpha = .4, size = 1.5, color = "grey") +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
geom_line(size = 2) +
scale_fill_manual(name = "trial type", values = palette) +
scale_color_manual(name = "trial type", values = palette) +
labs(x = expression("\nreactivity " * symbol('\254') * " signature expression " * symbol('\256') * " mindful attention"),
y = "craving rating\n") +
plot_aes +
theme(legend.position = "top")
within-person only
points_within = disaggregated_mindful %>%
select(pID, resp, trial_cond, dot_within_std) %>%
mutate(group = ifelse(trial_cond == "regulation", "mindful attention", "reactivity"),
group = factor(group, levels = c("reactivity", "mindful attention")),
type = "within-person") %>%
rename("x" = dot_within_std,
"predicted" = resp)
vals = modelr::seq_range(points_within$x, n = 25)
predicted = ggeffects::ggpredict(mod_ph_h2, c("dot_within_std [vals]", "trial_cond"),
ci.lvl = 0.9) %>%
data.frame() %>%
mutate(group = ifelse(group == "regulation", "mindful attention", "reactivity"),
group = factor(group, levels = c("reactivity", "mindful attention")))
predicted %>%
ggplot(aes(x, predicted, color = group, fill = group)) +
stat_smooth(data = points_within, aes(group = interaction(pID, group)), geom = "line", method = "lm", alpha = 0.15, se = FALSE, size = 1.25, fullrange = TRUE) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .3, color = NA) +
geom_line(size = 2) +
scale_fill_manual(name = "trial type", values = palette) +
scale_color_manual(name = "trial type", values = palette) +
labs(x = "within-person signature expression (SD)",
y = "craving rating\n",
title = "within-person") +
plot_aes +
theme(legend.position = "top")
table
make_table(mod_ph_h2)| term | b [90% CI] |
|---|---|
| intercept | 2.28 [1.97, 2.61] |
| task condition (mindful attention) | -0.21 [-0.61, 0.18] |
| signature expression (between) | 0.27 [0.03, 0.50] |
| signature expression (within) | -0.02 [-0.07, 0.04] |
| MAAS score | -0.05 [-0.23, 0.12] |
| task condition (mindful attention) x signature expression (between) | -0.46 [-0.85, -0.05] |
| task condition (mindful attention) x signature expression (within) | -0.01 [-0.09, 0.06] |
| task condition (mindful attention) x MAAS score | 0.00 [-0.09, 0.10] |
simple slopes
signature expression
emmeans::emtrends(mod_ph_h2, ~ trial_cond, var="dot_between_std_noc", level = .9) %>%
data.frame() %>%
mutate(`b [90% CI]` = sprintf("%.2f [%.2f, %.2f]", dot_between_std_noc.trend, lower.HPD, upper.HPD),
trial_cond = recode(trial_cond, "regulation" = "mindful attention")) %>%
select(trial_cond, `b [90% CI]`) %>%
knitr::kable()| trial_cond | b [90% CI] |
|---|---|
| reactivity | 0.27 [0.02, 0.49] |
| mindful attention | -0.20 [-0.52, 0.12] |
emmeans::emtrends(mod_ph_h2, ~ trial_cond, var="dot_within_std", level = .9) %>%
data.frame() %>%
mutate(`b [90% CI]` = sprintf("%.2f [%.2f, %.2f]", dot_within_std.trend, lower.HPD, upper.HPD),
trial_cond = recode(trial_cond, "regulation" = "mindful attention")) %>%
select(trial_cond, `b [90% CI]`) %>%
knitr::kable()| trial_cond | b [90% CI] |
|---|---|
| reactivity | -0.02 [-0.06, 0.04] |
| mindful attention | -0.03 [-0.09, 0.03] |
MAAS score
emmeans::emtrends(mod_ph_h2, ~ trial_cond, var="MAAS_mean", level = .9) %>%
data.frame() %>%
mutate(`b [90% CI]` = sprintf("%.2f [%.2f, %.2f]", MAAS_mean.trend, lower.HPD, upper.HPD),
trial_cond = recode(trial_cond, "regulation" = "mindful attention")) %>%
select(trial_cond, `b [90% CI]`) %>%
knitr::kable()| trial_cond | b [90% CI] |
|---|---|
| reactivity | -0.06 [-0.23, 0.12] |
| mindful attention | -0.05 [-0.24, 0.13] |
summary
summary(mod_ph_h2, prob = .9)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: resp ~ 1 + trial_cond * dot_between_std_noc + trial_cond * dot_within_std + trial_cond * MAAS_mean + (1 + trial_cond * dot_within_std | pID) + (1 | stimulus)
## Data: merged_ratings (Number of observations: 2193)
## Draws: 4 chains, each with iter = 1000; warmup = 500; thin = 1;
## total post-warmup draws = 2000
##
## Group-Level Effects:
## ~pID (Number of levels: 37)
## Estimate
## sd(Intercept) 0.60
## sd(trial_condregulation) 0.19
## sd(dot_within_std) 0.04
## sd(trial_condregulation:dot_within_std) 0.10
## cor(Intercept,trial_condregulation) -0.00
## cor(Intercept,dot_within_std) 0.21
## cor(trial_condregulation,dot_within_std) 0.05
## cor(Intercept,trial_condregulation:dot_within_std) -0.39
## cor(trial_condregulation,trial_condregulation:dot_within_std) 0.21
## cor(dot_within_std,trial_condregulation:dot_within_std) -0.17
## Est.Error
## sd(Intercept) 0.08
## sd(trial_condregulation) 0.07
## sd(dot_within_std) 0.03
## sd(trial_condregulation:dot_within_std) 0.05
## cor(Intercept,trial_condregulation) 0.26
## cor(Intercept,dot_within_std) 0.40
## cor(trial_condregulation,dot_within_std) 0.43
## cor(Intercept,trial_condregulation:dot_within_std) 0.30
## cor(trial_condregulation,trial_condregulation:dot_within_std) 0.39
## cor(dot_within_std,trial_condregulation:dot_within_std) 0.44
## l-90% CI u-90% CI
## sd(Intercept) 0.48 0.74
## sd(trial_condregulation) 0.07 0.31
## sd(dot_within_std) 0.00 0.10
## sd(trial_condregulation:dot_within_std) 0.03 0.18
## cor(Intercept,trial_condregulation) -0.41 0.44
## cor(Intercept,dot_within_std) -0.52 0.79
## cor(trial_condregulation,dot_within_std) -0.67 0.73
## cor(Intercept,trial_condregulation:dot_within_std) -0.81 0.16
## cor(trial_condregulation,trial_condregulation:dot_within_std) -0.49 0.80
## cor(dot_within_std,trial_condregulation:dot_within_std) -0.80 0.62
## Rhat Bulk_ESS
## sd(Intercept) 1.00 755
## sd(trial_condregulation) 1.01 455
## sd(dot_within_std) 1.00 937
## sd(trial_condregulation:dot_within_std) 1.01 941
## cor(Intercept,trial_condregulation) 1.00 2428
## cor(Intercept,dot_within_std) 1.00 2727
## cor(trial_condregulation,dot_within_std) 1.00 2477
## cor(Intercept,trial_condregulation:dot_within_std) 1.00 2522
## cor(trial_condregulation,trial_condregulation:dot_within_std) 1.00 1315
## cor(dot_within_std,trial_condregulation:dot_within_std) 1.00 1480
## Tail_ESS
## sd(Intercept) 1048
## sd(trial_condregulation) 321
## sd(dot_within_std) 985
## sd(trial_condregulation:dot_within_std) 983
## cor(Intercept,trial_condregulation) 1252
## cor(Intercept,dot_within_std) 1379
## cor(trial_condregulation,dot_within_std) 1667
## cor(Intercept,trial_condregulation:dot_within_std) 1417
## cor(trial_condregulation,trial_condregulation:dot_within_std) 1459
## cor(dot_within_std,trial_condregulation:dot_within_std) 1810
##
## ~stimulus (Number of levels: 72)
## Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.39 0.04 0.33 0.45 1.00 736 1326
##
## Population-Level Effects:
## Estimate Est.Error l-90% CI u-90% CI
## Intercept 2.28 0.19 1.97 2.61
## trial_condregulation -0.21 0.24 -0.61 0.18
## dot_between_std_noc 0.27 0.14 0.03 0.50
## dot_within_std -0.02 0.03 -0.07 0.04
## MAAS_mean -0.05 0.11 -0.23 0.12
## trial_condregulation:dot_between_std_noc -0.46 0.24 -0.85 -0.05
## trial_condregulation:dot_within_std -0.01 0.05 -0.09 0.06
## trial_condregulation:MAAS_mean 0.00 0.06 -0.09 0.10
## Rhat Bulk_ESS Tail_ESS
## Intercept 1.00 752 1012
## trial_condregulation 1.00 1502 1535
## dot_between_std_noc 1.00 1336 1197
## dot_within_std 1.00 3058 1751
## MAAS_mean 1.01 716 1118
## trial_condregulation:dot_between_std_noc 1.00 1945 1345
## trial_condregulation:dot_within_std 1.00 2728 1457
## trial_condregulation:MAAS_mean 1.00 2226 1332
##
## Family Specific Parameters:
## Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.85 0.01 0.82 0.87 1.00 3058 1415
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).compare posterior distributions
Compare preregistered model with the modeling controlling for MAAS scores
posterior = posterior_samples(mod_h2_int) %>%
transmute(`mindful attention`= b_dot_between_std_noc + `b_trial_condregulation:dot_between_std_noc`,
reactivity = b_dot_between_std_noc) %>%
gather(`trial type`, value) %>%
mutate(model = "original model") %>%
bind_rows(posterior_samples(mod_ph_h2) %>%
transmute(`mindful attention`= b_dot_between_std_noc + `b_trial_condregulation:dot_between_std_noc`,
reactivity = b_dot_between_std_noc) %>%
gather(`trial type`, value) %>%
mutate(model = "controlling for MAAS")) %>%
mutate(model = factor(model, levels = c("original model", "controlling for MAAS")))
posterior %>%
ggplot(aes(y = "", x = value, fill = model)) +
stat_halfeye(alpha = .5) +
scale_fill_manual(values = palette_group) +
facet_grid(~`trial type`) +
scale_y_discrete(expand = c(.1, .1)) +
labs(x = "\nregression coefficient\n", y = "") +
plot_aes